iops introduction
iops is mainly used in data. This indicator is an important reference for database performance evaluation. Iops refers to reading and writing (I/O) per second. ) The number of operations mainly depends on the performance of random access. Generally, in order to increase IOPS, disk arrays must be relied on. The actual online databases are basically configured with raid10. Raid5 cannot withstand the pressure in the actual production environment. Of course, You also need to check the specific business pressure. If you are using a physical machine, you need to see how much IOPS can reach in practice. Clouds are now common. If you use an RDS cloud database, you can choose the IOPS according to your business situation. , basically a parameter, which can be modified as needed. Of course, the larger the value, the higher the cost.
Python obtains the system iops code as follows:
#!/usr/bin/python import os, time, math run_tests = 3 devices = os.listdir('/sys/block/') check_devices = [] reads = {} writes = {} for dev in devices: if dev.startswith('md') or dev.startswith('sd') or dev.startswith('hd'): check_devices.append(dev) reads[dev] = [] writes[dev] = [] check_devices = sorted(check_devices) for t in range(run_tests + 1): for dev in check_devices: file_data = open('/sys/block/%s/stat' % dev).readline().strip().split(' ') clean = [] for num in file_data: if num != '': clean.append(int(num)) reads[dev].append(clean[0]) writes[dev].append(clean[4]) print reads[dev] print writes[dev] time.sleep(1) print "Device Read Write" print "--------------------------------------" for dev in check_devices: clean_reads = [] reads[dev].reverse() for test, result in enumerate(reads[dev]): if test > 0: clean_reads.append(float(reads[dev][test - 1] - result)) rops = int(math.ceil(sum(clean_reads) / len(clean_reads))) clean_writes = [] writes[dev].reverse() for test, result in enumerate(writes[dev]): if test > 0: clean_writes.append(float(writes[dev][test - 1] - result)) wops = int(math.ceil(sum(clean_writes) / len(clean_writes))) print "%s %s %s" % (dev.ljust(13), repr(rops).ljust(11), repr(wops))
Summary
The above is all the content of Python to obtain system iops. I hope this article will be helpful to everyone Learning and using python can be helpful to a certain extent. If you have any questions, you can leave a message to communicate.
For more articles related to how to obtain system iops in Python, please pay attention to the PHP Chinese website!

Create multi-dimensional arrays with NumPy can be achieved through the following steps: 1) Use the numpy.array() function to create an array, such as np.array([[1,2,3],[4,5,6]]) to create a 2D array; 2) Use np.zeros(), np.ones(), np.random.random() and other functions to create an array filled with specific values; 3) Understand the shape and size properties of the array to ensure that the length of the sub-array is consistent and avoid errors; 4) Use the np.reshape() function to change the shape of the array; 5) Pay attention to memory usage to ensure that the code is clear and efficient.

BroadcastinginNumPyisamethodtoperformoperationsonarraysofdifferentshapesbyautomaticallyaligningthem.Itsimplifiescode,enhancesreadability,andboostsperformance.Here'showitworks:1)Smallerarraysarepaddedwithonestomatchdimensions.2)Compatibledimensionsare

ForPythondatastorage,chooselistsforflexibilitywithmixeddatatypes,array.arrayformemory-efficienthomogeneousnumericaldata,andNumPyarraysforadvancednumericalcomputing.Listsareversatilebutlessefficientforlargenumericaldatasets;array.arrayoffersamiddlegro

Pythonlistsarebetterthanarraysformanagingdiversedatatypes.1)Listscanholdelementsofdifferenttypes,2)theyaredynamic,allowingeasyadditionsandremovals,3)theyofferintuitiveoperationslikeslicing,but4)theyarelessmemory-efficientandslowerforlargedatasets.

ToaccesselementsinaPythonarray,useindexing:my_array[2]accessesthethirdelement,returning3.Pythonuseszero-basedindexing.1)Usepositiveandnegativeindexing:my_list[0]forthefirstelement,my_list[-1]forthelast.2)Useslicingforarange:my_list[1:5]extractselemen

Article discusses impossibility of tuple comprehension in Python due to syntax ambiguity. Alternatives like using tuple() with generator expressions are suggested for creating tuples efficiently.(159 characters)

The article explains modules and packages in Python, their differences, and usage. Modules are single files, while packages are directories with an __init__.py file, organizing related modules hierarchically.

Article discusses docstrings in Python, their usage, and benefits. Main issue: importance of docstrings for code documentation and accessibility.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.

mPDF
mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Dreamweaver Mac version
Visual web development tools

EditPlus Chinese cracked version
Small size, syntax highlighting, does not support code prompt function
